# cEEG and rEEG detection rates of prognostic indicators in cardiac arrest patients: a systematic review and diagnostic meta-analysis

## TL;DR

This study compares the effectiveness of two EEG methods in predicting outcomes for cardiac arrest patients, finding that a shorter method may be just as useful.

## Contribution

The study provides a meta-analysis comparing diagnostic performance of cEEG and rEEG for neuroprognostication after cardiac arrest.

## Key findings

- cEEG showed high specificity (0.99) but moderate sensitivity (0.53) for prognostic indicators.
- rEEG demonstrated comparable specificity (0.97) with slightly lower sensitivity (0.50) but lower resource demands.
- rEEG may serve as a feasible alternative to cEEG in resource-limited settings.

## Abstract

Accurate neuroprognostication following cardiac arrest is essential for clinical decision-making; however, the comparative diagnostic performance of continuous electroencephalography (cEEG) and routine electroencephalography (rEEG) remains uncertain. Although cEEG is preferred for the detection of dynamic electrographic abnormalities such as nonconvulsive status epilepticus, the implementation of this technique is limited by high resource demands. Whether rEEG, a typically brief (20–30 min) recording, provides comparable prognostic accuracy is still debated.

We searched PubMed, Embase, Web of Science, and the Cochrane Library from January 2010 to December 2024 for studies relating to comatose post-cardiac arrest patients. Methodological quality was assessed using quality assessment of diagnostic accuracy studies 2 (QUADAS-2) and QUADAS-C. Statistical analyses were performed using Stata v18.0, with pooled sensitivity, specificity, and area under the summary receiver operating characteristic (SROC) curve (AUC) was estimated using random-effects models.

Sixteen studies (5,895 patients) were included. cEEG exhibited a pooled sensitivity of 0.53 [95% confidence interval (CI): 0.45–0.61] and specificity of 0.99 (95% CI: 0.97–1.00; AUC = 0.85). rEEG yielded a sensitivity of 0.50 (95% CI: 0.42–0.58) and a specificity of 0.97 (95% CI: 0.92–0.99; AUC = 0.75). Sensitivity analyses confirmed robustness while Deeks’ test indicated low publication bias (cEEG: p = 0.48; rEEG: p = 0.05).

Despite the theoretical advantages of cEEG in monitoring evolving brain activity, rEEG demonstrated comparable diagnostic performance, particularly in specificity, with substantially lower resource requirements. Our findings suggest that rEEG may serve as a feasible alternative or complementary tool to cEEG, especially in resource-constrained or time-sensitive settings, thereby supporting more accessible EEG-based neuroprognostication.

https://www.crd.york.ac.uk/PROSPERO/view/CRD420251151755, CRD420251151755.